Offline Skill Graph (OSG): A Framework for Learning and Planning using Offline Reinforcement Learning Skills
Ben-ya Halevy, Yehudit Aperstein, Dotan Di Castro

TL;DR
This paper introduces the Offline Skill Graph framework, enabling reinforcement learning agents to learn from offline data and plan over skills for complex, real-world tasks, demonstrated on a robotic arm.
Contribution
The paper presents a novel framework that combines offline skill learning with planning, addressing the gap in applying reinforcement learning to real-world scenarios.
Findings
Successfully applied to a robotic arm for complex tasks
Enables planning over offline learned skills
Addresses real-world application limitations
Abstract
Reinforcement Learning has received wide interest due to its success in competitive games. Yet, its adoption in everyday applications is limited (e.g. industrial, home, healthcare, etc.). In this paper, we address this limitation by presenting a framework for planning over offline skills and solving complex tasks in real-world environments. Our framework is comprised of three modules that together enable the agent to learn from previously collected data and generalize over it to solve long-horizon tasks. We demonstrate our approach by testing it on a robotic arm that is required to solve complex tasks.
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Taxonomy
TopicsData Stream Mining Techniques · Reinforcement Learning in Robotics · Multi-Agent Systems and Negotiation
